Learning with incrementality
This source preferred by Hamid Bouchachia
This data was imported from DBLP:
Authors: Bouchachia, A.
Editors: King, I., Wang, J., Chan, L. and Wang, D.L.
https://doi.org/10.1007/11893028
Journal: ICONIP (1)
Volume: 4232
Pages: 137-146
Publisher: Springer
This data was imported from Scopus:
Authors: Bouchachia, A.
Journal: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume: 4232 LNCS
Pages: 137-146
eISSN: 1611-3349
ISBN: 9783540464792
ISSN: 0302-9743
Learning with adaptivity is a key issue in many nowadays applications. The most important aspect of such an issue is incremental learning (IL). This latter seeks to equip learning algorithms with the ability to deal with data arriving over long periods of time. Once used during the learning process, old data is never used in subsequent learning stages. This paper suggests a new IL algorithm which generates categories. Each is associated with one class. To show the efficiency of the algorithm, several experiments are carried out. © Springer-Verlag Berlin Heidelberg 2006.
This data was imported from Web of Science (Lite):
Authors: Bouchachia, A.
Journal: NEURAL INFORMATION PROCESSING, PT 1, PROCEEDINGS
Volume: 4232
Pages: 137-146
eISSN: 1611-3349
ISSN: 0302-9743